Objective To examine the spatial heterogeneity of soil physicochemical and microbial properties at the county scale and their application in soil quality assessment, and offer a theoretical foundation for sustainable cultivated land use.

Method Surface soil samples of farmland from 47 monitoring units in Gaoyao district were collected. The spatial heterogeneity of soil physicochemical properties such as pH, clay/organic matter/total nitrogen/alkali-dissolved nitroge/total phosphorus contents, as well as soil microbial characteristics including soil respiration, mircrobial biomass, bacterial/fungal/actinomycetes biomass, and the ratio of fungi to bacteria were analyzed combining geostatistics and ArcGIS-related techniques. By employing principal component analysis and General Indicator of Soil Quality (GISQ) method, we elucidated the influence of different factors on the comprehensive quality of farmland.

Result 1)The nugget coefficients of soil pH, clay/organic matter/total nitrogen/alkali-hydrolyzable nitrogen/total phosphorus contents ranged from 25% to 75%, indicating mederate spatial autocorrelation, and was affected by both structural and random factors. Among the soil microbial indicators, the nugget coefficient of soil respiration was 29.4%, indicating mederate spatial autocorrelation, but the nugget coefficients of the total soil microbial biomass, fungi/actinomycetes/bacterial biomass and the fungi to bacteria ratio were all greater than 75%, indicating weak spatial autocorrelation and poor spatial structure, and influenced by random factors such as human activities. 2) Soil microorganisms were the primary driving factors of soil quality differentiation of farmland in Gaoyao District, especially the total soil mricrobial biomass, bacterial/fungi/actinomycete biomass, which have significant influences on soil quality. Physicochemical properties such as soil organic matter, total nitrogen and alkali-dissolved contents also had considerable impact on farmland soil quality. Additionally, organic matter, total nitrogen and alkali-dissolved contents were significantly positively correlated (P<0.05), and soil respiration was significantly positively correlated with organic matter and alkali-dissolved contents. 3) The results of the GISQ method indicate the following ranking in terms of soil comprehensive quality: Northern hilly area > eastern plain area > central plain area > southern hilly area.

Conclusion At the county scale, the spatial structure of soil physicochemical properties is relatively stable, and soil respiration is a suitable indicator for analyzing microbial spatial variability. Soil microbial biomass and its structure exhibit significant spatial heterogeneity at the county scale. The combined application of soil physicochemical and microbial indicators in soil quality evaluation can more comprehensively reflect the changes in farmland quality.